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Record W2160715838 · doi:10.1145/1806799.1806828

Using information fragments to answer the questions developers ask

2010· article· en· W2160715838 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAsk priceComputer scienceWorld Wide WebInformation retrievalBusiness

Abstract

fetched live from OpenAlex

Each day, a software developer needs to answer a variety of questions that require the integration of different kinds of project information. Currently, answering these questions, such as “What have my co-workers been doing?”, is tedious, and sometimes impossible, because the only support available requires the developer to manually link and traverse the information step-by-step. Through interviews with eleven professional developers, we identified 78 questions developers want to ask, but for which support is lacking. We introduce an information fragment model (and prototype tool) that automates the composition of different kinds of information and that allows developers to easily choose how to display the composed information. In a study, 18 professional developers used the prototype tool to answer eight of the 78 questions. All developers were able to easily use the prototype to successfully answer 94 % of questions in a mean time of 2.3 minutes per question.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.631
Threshold uncertainty score0.306

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.295
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations158
Published2010
Admission routes1
Has abstractyes

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